An approach to direction finding based on a subspace perturbation expansion
Date of Original Version
This paper describes a method for estimating directions of arrival from sensor-array data. The estimates are obtained from minimizing a subspace-fitting cost function. This cost function is optimally weighted using statistical information provided by a subspace perturbation expansion. The calculations for scenarios involving very closely spaced sources suffers from the need to invert ill conditioned matrices. This problem is overcome by a reparameterization of both the cost function and its Jacobian based on the concept of a limiting subspace. The paper includes a challenging simulation example involving multiple moving sources.
Conference Record of the Asilomar Conference on Signals, Systems and Computers
Vaccaro, Richard J., Pranab Majumdar, and Norman L. Owsley. "An approach to direction finding based on a subspace perturbation expansion." Conference Record of the Asilomar Conference on Signals, Systems and Computers 1, (2003): 817-821. https://digitalcommons.uri.edu/ele_facpubs/1146